IEEE Trans Med Imaging. 2020 Jul;39(7):2289-2301. doi: 10.1109/TMI.2020.2968472. Epub 2020 Jan 21.
Computed tomography (CT) is a widely used screening and diagnostic tool that allows clinicians to obtain a high-resolution, volumetric image of internal structures in a non-invasive manner. Increasingly, efforts have been made to improve the image quality of low-dose CT (LDCT) to reduce the cumulative radiation exposure of patients undergoing routine screening exams. The resurgence of deep learning has yielded a new approach for noise reduction by training a deep multi-layer convolutional neural networks (CNN) to map the low-dose to normal-dose CT images. However, CNN-based methods heavily rely on convolutional kernels, which use fixed-size filters to process one local neighborhood within the receptive field at a time. As a result, they are not efficient at retrieving structural information across large regions. In this paper, we propose a novel 3D self-attention convolutional neural network for the LDCT denoising problem. Our 3D self-attention module leverages the 3D volume of CT images to capture a wide range of spatial information both within CT slices and between CT slices. With the help of the 3D self-attention module, CNNs are able to leverage pixels with stronger relationships regardless of their distance and achieve better denoising results. In addition, we propose a self-supervised learning scheme to train a domain-specific autoencoder as the perceptual loss function. We combine these two methods and demonstrate their effectiveness on both CNN-based neural networks and WGAN-based neural networks with comprehensive experiments. Tested on the AAPM-Mayo Clinic Low Dose CT Grand Challenge data set, our experiments demonstrate that self-attention (SA) module and autoencoder (AE) perceptual loss function can efficiently enhance traditional CNNs and can achieve comparable or better results than the state-of-the-art methods.
计算机断层扫描（CT）是一种广泛使用的筛查和诊断工具，它允许临床医生以非侵入性的方式获得内部结构的高分辨率、容积图像。越来越多的人努力提高低剂量 CT（LDCT）的图像质量，以减少接受常规筛查检查的患者的累积辐射暴露。深度学习的复兴为降低噪声提供了一种新方法，即通过训练深度多层卷积神经网络（CNN）将低剂量到正常剂量 CT 图像映射来实现。然而，基于 CNN 的方法严重依赖于卷积核，卷积核使用固定大小的滤波器来一次处理感受野内的一个局部邻域。因此，它们在检索大区域的结构信息方面效率不高。在本文中，我们提出了一种用于 LDCT 去噪问题的新型 3D 自注意力卷积神经网络。我们的 3D 自注意力模块利用 CT 图像的 3D 体积来捕获 CT 切片内和切片之间的广泛的空间信息。借助 3D 自注意力模块，CNN 能够利用关系更强的像素，而无需考虑其距离，从而获得更好的去噪效果。此外，我们提出了一种自监督学习方案，以训练特定领域的自动编码器作为感知损失函数。我们将这两种方法结合起来，并通过全面的实验证明了它们在基于 CNN 的神经网络和基于 WGAN 的神经网络上的有效性。在 AAPM-Mayo 诊所低剂量 CT 大挑战数据集上进行测试，我们的实验表明，自注意力（SA）模块和自动编码器（AE）感知损失函数可以有效地增强传统 CNN，并可以获得与最先进方法相当或更好的结果。
